Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Duk Shin, Hiroyuki Kambara, Natsue Yoshimura, Yasuharu Koike
2018 Computational Intelligence and Neuroscience  
Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit
more » ... etween band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.
doi:10.1155/2018/2580165 pmid:30420874 pmcid:PMC6211210 dblp:journals/cin/ShinKYK18 fatcat:e73ekponanebbdgrnqhg5yizra